Machine learning systems are often described as learning from experience either with or without supervision from humans. In supervised learning problems, a program predicts an output for an input by learning from pairs of labeled inputs and outputs. That is, the program learns from examples of the "right answers". In unsupervised learning, a program does not learn from labeled data. Instead, it attempts to discover patterns in data. For example, assume that you have collected data describing the heights and weights of people. An example of an unsupervised learning problem is dividing the data points into groups. A program might produce groups that correspond to men and women, or children and adults. Now assume that the data is also labeled with the person's sex. An example of a supervised learning problem is to induce a rule for predicting whether a person is male or female based on his or her height and weight. We will discuss algorithms and examples of supervised and unsupervised learning in the following chapters.
Supervised learning and unsupervised learning can be thought of as occupying opposite ends of a spectrum. Some types of problem, called semi-supervised learning problems, make use of both supervised and unsupervised data; these problems are located on the spectrum between supervised and unsupervised learning. Reinforcement learning is located near the supervised end of the spectrum. Unlike supervised learning, reinforcement learning programs do not learn from labeled pairs of inputs and outputs. Instead, they receive feedback for their decisions, but errors are not explicitly corrected. For example, a reinforcement learning program that is learning to play a side-scrolling video game like Super Mario Bros may receive a reward when it completes a level or exceeds a certain score, and a punishment when it loses a life. However, this supervised feedback is not associated with specific decisions to run, avoid Goombas, or pick up fire flowers. We will focus primarily on supervised and unsupervised learning, as these categories include most common machine learning problems. In the next sections, we will review supervised and unsupervised learning in more detail.
A supervised learning program learns from labeled examples of the outputs that should be produced for an input. There are many names for the output of a machine learning program. Several disciplines converge in machine learning, and many of those disciplines use their own terminology. In this book, we will refer to the output as the response variable. Other names for response variables include "dependent variables", "regressands", "criterion variables", "measured variables", "responding variables", "explained variables", "outcome variables", "experimental variables", "labels", and "output variables". Similarly, the input variables have several names. In this book, we will refer to inputs as features, and the phenomena they represent as explanatory variables. Other names for explanatory variables include "predictors", "regressors", "controlled variables", and "exposure variables". Response variables and explanatory variables may take real or discrete values.
The collection of examples that comprise supervised experience is called a training set. A collection of examples that is used to assess the performance of a program is called a test set. The response variable can be thought of as the answer to the question posed by the explanatory variables; supervised learning problems learn from a collection of answers to different questions. That is, supervised learning programs are provided with the correct answers and must learn to respond correctly to unseen, but similar, questions.
